Abstract
In this paper, we analyze the possibility to identify people using online handwritten tasks different from the classic ones (signature and text). Our preliminary results reveal that some drawings offer a reasonably good identification rate, which is 78.5% in the case of multi-sectional vector quantization applied to classify circles and house drawing tasks. To the best of our knowledge, this is the first paper devoted to biometric recognition based on drawing tasks.
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Acknowledgements
This work has been supported by FEDER and MEC, TEC2016-77791-C4-2-R.
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Lopez-Xarbau, J., Faundez-Zanuy, M., Garnacho-Castaño, M. (2020). Preliminary Study on Biometric Recognition Based on Drawing Tasks. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, vol 151. Springer, Singapore. https://doi.org/10.1007/978-981-13-8950-4_43
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DOI: https://doi.org/10.1007/978-981-13-8950-4_43
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